arXiv AI
FireWorldBench: Benchmarking Complex Physical World Intelligence through Coupled-Field Fire Dynamics
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arXiv AI
Jul 31
CG-World: A Large-Scale World-State Dataset and Protocol for World Models
arXiv:2607. 26452v1 Announce Type: new Abstract: World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure.
Hugging Face Trending Papers
Jul 29
CG-World: A Large-Scale World-State Dataset and Protocol for World Models
World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure. We introduce CG-World, a large-scale world-state dataset and protocol derived from industrial computer graphics production pipelines.
arXiv Computer Vision
2d ago
PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop
PhysVista is a new benchmark that evaluates physical intelligence in Vision‑Language Models (VLMs) by integrating perception, reasoning, and plausibility assessment into a closed cognitive loop. It distinguishes between event‑level and scale‑level reasoning and tests models on both real‑world and AI‑generated videos to provide a holistic, fine‑grained analysis of physical understanding. Experiments show significant gaps in VLMs’ physical reasoning and plausibility assessment, underscoring the need for more principled, physically grounded multimodal designs.
arXiv AI
Jul 14
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
arXiv:2607. 11689v1 Announce Type: cross Abstract: Artificial general intelligence ultimately requires agents that can reason and act in the physical world.
arXiv AI
Sep 25
Planning Takes More Than Token Prediction: Causal Plan for Benchmarking and Building Physically Grounded Embodied Reasoners
The paper argues that current embodied vision‑language planning benchmarks favor linguistic next‑token prediction over physically grounded next‑state reasoning, leading models to rely on language priors rather than true causal dependencies. To address this, the authors introduce Causal‑Plan‑Bench, a diagnostic suite covering four causal dimensions, and Causal‑Plan‑1M, a million‑scale corpus of explicit causal reasoning traces extracted from egocentric videos. Extensive experiments show that existing models perform poorly on these tasks, while a new model trained with a tailored recipe—Causal Planner based on Qwen3‑VL‑8B—achieves significant gains, demonstrating the feasibility of physically grounded causal reasoning.
arXiv AI
Jun 9
SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
arXiv:2606. 09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world.